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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Single feature polymorphism detection using recombinant inbred line microarray expression data.

Xinping Cui1, Na You, Thomas Girke

  • 1Department of Statistics, University of California, Riverside, CA, USA. xinping.cui@ucr.edu

Bioinformatics (Oxford, England)
|June 26, 2010
PubMed
Summary
This summary is machine-generated.

We developed a robust method for detecting single feature polymorphisms (SFPs) in microarray data from recombinant inbred lines (RILs). This approach significantly increases SFP discovery and improves genetic map accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Affymetrix GeneChip microarrays offer a high-density, economical platform for genetic polymorphism discovery.
  • Recombinant inbred lines (RILs) provide high locus replication, ideal for single feature polymorphism (SFP) detection.
  • SFP detection in RILs may involve multimodal binding affinity distributions, necessitating robust statistical methods.

Purpose of the Study:

  • To develop and validate a hypothesis testing procedure for SFP detection using microarray data.
  • To estimate binding affinities using Robust Multi-array Analysis (RMA) and formulate SFP detection as a statistical test.
  • To identify SFPs by testing for unimodal versus multimodal distributions of estimated binding affinity (EBA) values.

Main Methods:

  • Developed a bootstrap-based hypothesis testing procedure utilizing the 'dip' statistic.
  • Employed Robust Multi-array Analysis (RMA) for estimating binding affinities.
  • Formulated SFP detection as a hypothesis test comparing unimodal and multimodal distributions.

Main Results:

  • The procedure demonstrated satisfactory detection power with controlled false discovery rates in simulations.
  • The method is robust to the unimodal distribution assumption, enabling wide application.
  • Identified over four times more SFPs than previous studies, encompassing 96% of prior findings.
  • Generated a genetic map with >99% concordance between marker genetic orders and physical locations.

Conclusions:

  • The developed bootstrap hypothesis testing procedure is effective for SFP detection in RILs.
  • The 'dipSFP' R package provides a powerful tool for enhanced SFP discovery and genetic mapping.
  • This method significantly advances the utility of microarray data for genetic analysis.